The Impact of Land—Use Composition and Landscape Pattern on Water Quality at Different Spatial Scales in the Dan River Basin, Qin Ling Mountains
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Preparation
2.2. Data Analysis
3. Results
3.1. Temporal and Spatial Distributions of Water Quality
3.2. Land—Use Composition and Landscape Pattern Indices
3.3. Impact of Land—Use Structure and Landscape Pattern on Water Quality
3.3.1. Effects of Land—Use Structure on Water Quality
3.3.2. RDA of Land—Use Composition and Landscape Pattern Indices to Water Quality
4. Discussion
4.1. Effects of Land—Use Structure on Water Quality
4.2. Effects of Landscape Pattern on Water Quality
4.3. Optimization of Water Quality and Landscape Pattern
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Value | Pollution Level |
---|---|
≤0.20 | clean |
0.21~0.40 | Still clean |
0.41~0.70 | Mild pollution |
0.71~1.00 | Moderate pollution |
1.01~2.00 | Heavy pollution |
>2.00 | Serious pollution |
Landscape Pattern Index | Ecological Significance |
---|---|
Patch density (PD) | Describing the degree of spatial heterogeneity and fragmentation of the landscape, and the larger the value, the higher the degree of heterogeneity and fragmentation. |
Patch richness density (PRD) | Referring to the abundance of patches per unit area, which reflects the diversity of patch types within the landscape, the more abundant the patches, the stronger the ability to resist external disturbances. |
Largest patch index (LPI) | Reflecting the dominant type of landscape, the value range is (0,100), and the size of its value determines the landscape dominant species and internal species abundance. And it also reflects the direction and strength of human activities. |
Contagion index (CONTAG) | Reflecting the degree of agglomeration or extension trend of different patch types in the landscape, with a value range of (0,100). The high contagion value indicates that a certain dominant patch type in a landscape forms a good connectivity; otherwise, it indicates that the landscape is a dense pattern with many elements and has a high degree of fragmentation. |
Shannon’s diversity index (SHDI) | Reflecting the heterogeneity of landscape systems. The large SHDI value indicates that the degree of landscape fragmentation is high, and there are many types of blocks in the landscape or the distribution of each block type in the landscape is balanced. |
Landscape shape index (LSI) | Indicates the degree of plaque regularity, and a large value indicates that the plaque is irregularly shaped. |
Index | Maximum | Minimum | Mean Value | SD | CV (%) | |
---|---|---|---|---|---|---|
DO (mg/L) | Dry | 5.387 | 6.400 | 5.936 | 0.226 | 3.81 |
Wet | 5.418 | 6.737 | 6.021 | 0.336 | 5.58 | |
COD (mg/L) | Dry | 5.509 | 12.010 | 8.636 | 1.726 | 19.99 |
Wet | 1.533 | 7.117 | 4.157 | 1.444 | 34.73 | |
TN (mg/L) | Dry | 2.097 | 5.736 | 4.236 | 1.065 | 25.14 |
Wet | 1.876 | 4.849 | 3.495 | 0.979 | 27.99 | |
TP (mg/L) | Dry | 0.073 | 0.112 | 0.094 | 0.011 | 11.29 |
Wet | 0.031 | 0.081 | 0.051 | 0.013 | 25.76 | |
NH3–N (mg/L) | Dry | 0.032 | 0.301 | 0.152 | 0.066 | 43.38 |
Wet | 0.063 | 0.140 | 0.092 | 0.020 | 22.30 | |
NO3–N (mg/L) | Dry | 1.707 | 5.166 | 3.595 | 1.175 | 32.67 |
Wet | 1.759 | 3.371 | 2.416 | 0.511 | 21.15 | |
NO2–N (mg/L) | Dry | 0.032 | 0.089 | 0.060 | 0.012 | 19.83 |
Wet | 0.006 | 0.036 | 0.014 | 0.008 | 60.11 | |
Cr6+ (μg/L) | Dry | 2.955 | 9.693 | 6.731 | 1.876 | 27.87 |
Wet | 2.053 | 6.088 | 4.047 | 1.046 | 25.85 | |
TOC (mg/L) | Dry | 1.759 | 3.441 | 2.439 | 0.488 | 20.00 |
Wet | 0.947 | 1.860 | 1.291 | 0.240 | 18.63 |
References
- Hu, X.; Wang, H.; Zhu, Y.; Xie, G.; Shi, H. Landscape Characteristics Affecting Spatial Patterns of Water Quality Variation in a Highly Disturbed Region. Int. J. Environ. Res. Public Health 2019, 16, 2149. [Google Scholar] [CrossRef]
- Galbraith, L.M.; Burns, C.W. Linking Land—Use, Water Body Type and Water Quality in Southern New Zealand. Landsc. Ecol. 2007, 22, 231–241. [Google Scholar] [CrossRef]
- Turner, R.E.; Rabalais, N.N. Linking Landscape and Water Quality in the Mississippi River Basin for 200 Years. BioScience 2003, 53, 563–572. [Google Scholar] [CrossRef]
- Tim, U.S.; Jolly, R. Evaluating Agricultural Nonpoint—Source Pollution Using Integrated Geographic Information Systems and Hydrologic/Water Quality Model. J. Environ. Qual. 1994, 23, 25–35. [Google Scholar] [CrossRef]
- de Oliveira, L.M.; Maillard, P.; de Andrade Pinto, E.J. Application of a land cover pollution index to model non—Point pollution sources in a Brazilian watershed. Catena 2017, 150, 124–132. [Google Scholar] [CrossRef]
- Ouyang, W.; Skidmore, A.K.; Toxopeus, A.G.; Hao, F. Long-term vegetation landscape pattern with non-point source nutrient pollution in upper stream of Yellow River basin. J. Hydrol. 2010, 389, 373–380. [Google Scholar] [CrossRef]
- Basnyat, P.; Teeter, L.D.; Flynn, K.M.; Lockaby, B.G. Relationships Between Landscape Characteristics and Nonpoint Source Pollution Inputs to Coastal Estuaries. Environ. Manag. 1999, 23, 539–549. [Google Scholar] [CrossRef]
- Giri, S.; Qiu, Z. Understanding the relationship of land uses and water quality in Twenty First Century: A review. J. Environ. Manag. 2016, 173, 41–48. [Google Scholar] [CrossRef]
- Liu, Y.N.; Kong, L.Q.; Xiao, Y.; Zheng, H. Relationships between landscape pattern and ecosystem water purification service in the Yangtze River Basin. Acta Ecol. Sin. 2019, 39, 844–852. [Google Scholar] [CrossRef]
- Bu, H.; Meng, W.; Zhang, Y.; Wan, J. Relationships between land use patterns and water quality in the Taizi River basin, China. Ecol. Indic. 2014, 41, 187–197. [Google Scholar] [CrossRef]
- Plexida, S.G.; Sfougaris, A.I.; Ispikoudis, I.P.; Papanastasis, V.P. Selecting landscape metrics as indicators of spatial heterogeneity—A comparison among Greek landscapes. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 26–35. [Google Scholar] [CrossRef]
- Jie, Y.; Youpeng, X.; Bin, G.; Yuefeng, W.; Yu, X.; Qian, M. River water quality change and its relationship with landscape pattern under the urbanization: A case study of Suzhou City in Taihu Basin. J. Lake Sci. 2017, 29, 827–835. [Google Scholar] [CrossRef]
- Wei, F.; Mei-hua, H.; Lan-ying, W.; Wei, W.; Juan, X.; Guo-shuang, Z.; Jun-ju, Z.; Guo-feng, Z. Relationship between landscape pattern and hydrochemical characteristics of Binggou River Basin in eastern Qilian Mountains. Chin. J. Ecol. 2019, 38, 3779–3788. [Google Scholar] [CrossRef]
- Shi, P.; Zhang, Y.; Li, Z.; Li, P.; Xu, G. Influence of land use and land cover patterns on seasonal water quality at multi—spatial scales. Catena 2017, 151, 182–190. [Google Scholar] [CrossRef]
- Zhang, Y.J.; Chen, S.; Xiang, J.C. Correlation between the water quality and land use composition in the river side area—A case of Chaohu Lake Basin in China. Resour. Environ. Yangtze Basin 2011, 20, 1054–1061. [Google Scholar]
- Li, K.; Wang, L.; Sun, W.; Wang, X.R.; Li, Z.H. Spatial effect of landscape pattern on river water quality under urbanization. Acta Sci. Circumstantiae 2020, 40, 343–352. [Google Scholar] [CrossRef]
- Mello, K.d.; Valente, R.A.; Randhir, T.O.; dos Santos, A.C.A.; Vettorazzi, C.A. Effects of land use and land cover on water quality of low—order streams in Southeastern Brazil: Watershed versus riparian zone. CATENA 2018, 167, 130–138. [Google Scholar] [CrossRef]
- Zhang, J.; Li, S.; Dong, R.; Jiang, C.; Ni, M. Influences of land use metrics at multi—spatial scales on seasonal water quality: A case study of river systems in the Three Gorges Reservoir Area, China. J. Clean. Prod. 2019, 206, 76–85. [Google Scholar] [CrossRef]
- HU Lin, L.S. Scale Effects of Land Use Structure and Landscape Pattern on Water Quality in the Longchuan River Basin. J. Ecol. Environ. Sci. 2021, 30, 1470–1481. [Google Scholar] [CrossRef]
- Dong, X.H.; Yang, X.D.; Liu, E.F.; Wang, R. Application of redundancy analysis in predigesting sedimentary proxies for paleolimnological research: A case study of Taibai Lake. Geogr. Res. 2007, 26, 477–484. [Google Scholar] [CrossRef]
- Li, S.; Gu, S.; Liu, W.; Han, H.; Zhang, Q. Water quality in relation to land use and land cover in the upper Han River Basin, China. CATENA 2008, 75, 216–222. [Google Scholar] [CrossRef]
- Shi, B.; Bach, P.M.; Lintern, A.; Zhang, K.; Coleman, R.A.; Metzeling, L.; McCarthy, D.T.; Deletic, A. Understanding spatiotemporal variability of in—Stream water quality in urban environments—A case study of Melbourne, Australia. J. Environ. Manag. 2019, 246, 203–213. [Google Scholar] [CrossRef] [PubMed]
- Winston, R.J.; Hunt, W.F.; Osmond, D.L.; Lord, W.G.; Woodward, M.D. Field Evaluation of Four Level Spreader–Vegetative Filter Strips to Improve Urban Storm—Water Quality. J. Irrig. Drain. Div. 2011, 137, 170–182. [Google Scholar] [CrossRef]
- Wei, W.; Gao, Y.; Huang, J.; Gao, J. Exploring the effect of basin land degradation on lake and reservoir water quality in China. J. Clean. Prod. 2020, 268, 122249. [Google Scholar] [CrossRef]
- Sliva, L.; Williams, D.D. Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Res. 2001, 35, 3462–3472. [Google Scholar] [CrossRef] [PubMed]
- Johnson, L.; Richards, C.; Host, G.; Arthur, J. Landscape influences on water chemistry in Midwestern stream ecosystems. Freshw. Biol. 1997, 37, 193–208. [Google Scholar] [CrossRef]
- Collins, K.E.; Doscher, C.; Rennie, H.G.; Ross, J.G. The Effectiveness of Riparian ‘Restoration’ on Water Quality—A Case Study of Lowland Streams in Canterbury, New Zealand. Restor. Ecol. 2013, 21, 40–48. [Google Scholar] [CrossRef]
- Xu, G.; Li, P.; Lu, K.; Tantai, Z.; Zhang, J.; Ren, Z.; Wang, X.; Yu, K.; Shi, P.; Cheng, Y. Seasonal changes in water quality and its main influencing factors in the Dan River basin. CATENA 2019, 173, 131–140. [Google Scholar] [CrossRef]
- Ministry of Ecology and Environment of the People’s Republic of China. Environmental Quality Standards for Surface Water; MEE: Beijing, China, 2002; GB3838−2002.
- Guo, J.; Wang, C.M.; Huang, D.Z.; Li, Q.; Lian, H. Pollution characterization and water quality assessment of Dongting Lake. Environ. Chem. 2019, 38, 152–160. [Google Scholar] [CrossRef]
- Xu, Y.; Fu, B.; Lü, Y. Research on landscape pattern and ecological processes based on landscape models. Acta Ecol. Sin. 2010, 30, 212–220. [Google Scholar] [CrossRef]
- Gillies, R.R.; Kustas, W.P.; Humes, K.S. A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e. Int. J. Remote Sens. 1997, 18, 3145–3166. [Google Scholar] [CrossRef]
- Huang, Z.; Han, L.; Zeng, L.; Xiao, W.; Tian, Y. Effects of land use patterns on stream water quality: A case study of a small—Scale watershed in the Three Gorges Reservoir Area, China. Environ. Sci. Pollut. Res. Int. 2016, 23, 3943–3955. [Google Scholar] [CrossRef] [PubMed]
- Wu, J.G. Landscape Ecology: Pattern, Process, Scale and Hierarchy, 2nd ed.; Higher Education Press: Beijing, China, 2007; p. 266. ISBN 978−7−04−020879−5. [Google Scholar]
- Xizhi, L.; Jing, H.; Peiqing, X.; Pan, Z. Correlation analysis between the water quality and land use composition in chaobai river basin. Nat. Environ. Pollut. Technol. 2017, 16, 307–314. [Google Scholar]
- Zeng, Y.; Jin, W.; Wang, H.; Zhang, H. Simulation of land-use changes and landscape ecological assessment in eastern part of Qinghai Plateau. Trans. Chin. Soc. Agric. Eng. 2014, 30, 185–194. [Google Scholar] [CrossRef]
- ter Braak, C.J.F.; Smilauer, P. Canoco Reference Manual and Users Guide: Software for Ordination, Version 5.0.; Microcomputer Power: Ithaca, NY, USA, 2012; p. 496. [Google Scholar]
- Fedorko, E.J.; Pontius, R.G., Jr.; Aldrich, S.P.; Claussens, L.; Hopkinson, C.S.; Wollheim, W.M. Spatial Distribution of Land Type in Regression Models of Pollutant Loading. Biol. Bull. 2004, 207, 173. [Google Scholar] [CrossRef] [PubMed]
- Huang, J.L.; Li, Q.S.; Hong, H.S.; Lin, J.; Qu, M.C. Preliminary study on linking land use & landscape pattern and water quality in the Jiulong River watershed. Huanjing Kexue 2011, 32, 64–72. [Google Scholar]
- Ji, D.Q.; Wang, Y.; We, J.B.; Wu, Z.F.; Liu, Q.; Cheng, J. Relationships between landscape spatial characteristics and surface water quality in the Liu Xi River watershed. Acta Ecol. Sin. 2015, 35, 246–253. [Google Scholar] [CrossRef]
- Guo, Y.Y.; Li, S.Y.; Liu, R.; Zhang, J. Relationship between landscape pattern and water quality of the multi-scale effects in the Yellow River Basin. J. Lake Sci. 2021, 33, 737–748. [Google Scholar] [CrossRef]
- Cui, H.; Zhou, X.D.; Guo, M.L.; Wu, W. Land use change and its effects on water quality in typical inland lake of arid area in China. J. Environ. Biol. 2016, 37, 603–609. [Google Scholar]
- Tong, S.T.Y.; Chen, W. Modeling the relationship between land use and surface water quality. J. Environ. Manag. 2002, 66, 377–393. [Google Scholar] [CrossRef]
- Jung, K.-W.; Lee, S.-W.; Hwang, H.-S.; Jang, J.-H. The effects of spatial variability of land use on stream water quality in a costal watershed. Paddy Water Environ. 2008, 6, 275–284. [Google Scholar] [CrossRef]
- Yujing, G.; Yan, W.; Yungen, L.; Yi, Z.; Chao, Z.; Lei, H. The effects of landscape pattern evolution in Puzhehei karst lake wetland littoral zone on water quality. Acta Ecol. Sin. 2018, 38. [Google Scholar] [CrossRef]
- Bian, Z.Q.; Liu, Y.Y.; Ding, S.Y. Correlation between Spatial-Temporal Variation in Landscape Patterns and Surface Water Quality: A Case Study in the Yi River Watershed, China. Appl. Sci. 2019, 9, 1053. [Google Scholar] [CrossRef]
- Wang, J.; li, P.; Gao, H.D.; Shi, P.; Zhang, Q.L.; Yang, Q.N.; Ma, Y.Y. Preliminary study on the relationship between land use /landscape index and water quality in the upper reaches of Danjiang river. Res. Soil. Water Conserv. 2018, 25, 383–389. [Google Scholar] [CrossRef]
- Peng, J.; Liu, Y.; Corstanje, R.; Meersmans, J. Promoting sustainable landscape pattern for landscape sustainability. Landsc. Ecol. 2021, 36, 1839–1844. [Google Scholar] [CrossRef]
- Song, Y.; Song, X.; Shao, G. Response of Water Quality to Landscape Patterns in an Urbanized Watershed in Hangzhou, China. Sustainability 2020, 12, 5500. [Google Scholar] [CrossRef]
Spatial Scale | Index | Mean Value | SD | Maximum | Minimum |
---|---|---|---|---|---|
Riverine reach | LPI | 40.21 | 12.52 | 18.90 | 59.46 |
PD | 2.43 | 0.40 | 1.87 | 3.02 | |
LSI | 3.97 | 0.66 | 3.01 | 4.97 | |
CONTAG | 48.98 | 5.54 | 41.40 | 58.34 | |
PRD | 0.54 | 0.06 | 0.43 | 0.58 | |
SHDI | 1.07 | 0.11 | 0.94 | 1.26 | |
Riparian | LPI | 18.88 | 6.26 | 9.24 | 29.99 |
PD | 1.59 | 0.22 | 1.34 | 2.01 | |
LSI | 23.08 | 3.30 | 16.66 | 28.79 | |
CONTAG | 54.34 | 3.06 | 47.94 | 58.30 | |
PRD | 0.02 | 0.01 | 0.01 | 0.03 | |
SHDI | 1.16 | 0.07 | 1.07 | 1.27 | |
Substream | LPI | 17.73 | 5.57 | 11.00 | 29.95 |
PD | 0.72 | 0.10 | 0.59 | 0.90 | |
LSI | 29.57 | 3.86 | 21.43 | 35.80 | |
CONTAG | 57.66 | 2.53 | 54.08 | 62.82 | |
PRD | 0.01 | 0.00 | 0.00 | 0.01 | |
SHDI | 1.10 | 0.05 | 1.02 | 1.19 |
Spatial Scale | Land Use | Season | DO | COD | TN | TP | NH3–N | NO3–N | NO2–N | Cr6+ | TOC |
---|---|---|---|---|---|---|---|---|---|---|---|
Riverine reach | Argi | dry | −0.15 | 0.48 * | 0.72 ** | 0.39 | 0.42 | 0.77 ** | 0.55 * | 0.39 | 0.58 ** |
wet | −0.51 * | 0.29 | 0.70 ** | 0.47 * | 0.39 | 0.37 | 0.76 ** | 0.21 | 0.01 | ||
Forest | dry | 0.44 | −0.07 | −0.53 * | −0.69 ** | −0.29 | −0.15 | 0.03 | 0.15 | 0.34 | |
wet | −0.09 | 0.09 | −0.50 * | −0.33 | −0.35 | −0.23 | −0.10 | −0.34 | 0.19 | ||
Grass | dry | −0.06 | 0.16 | −0.58 ** | −0.75 ** | −0.34 | −0.12 | −0.10 | 0.35 | 0.01 | |
wet | 0.29 | −0.42 | −0.58 ** | −0.79 ** | −0.61 ** | 0.38 | 0.32 | −0.07 | −0.29 | ||
Water | dry | 0.13 | −0.38 | −0.52* | −0.79 ** | −0.20 | −0.29 | −0.26 | −0.31 | −0.53 * | |
wet | 0.65 ** | −0.75* | −0.74 ** | −0.77 ** | −0.63 ** | −0.01 | −0.17 | −0.46 * | −0.40 | ||
Constr | dry | −0.12 | −0.35 | 0.33 | 0.71 ** | 0.08 | −0.14 | −0.29 | −0.47 * | −0.18 | |
wet | −0.20 | 0.29 | 0.46* | 0.58 ** | 0.40 | −0.28 | −0.60 ** | 0.18 | 0.26 | ||
Riparian | Argi | dry | 0.22 | 0.16 | 0.49* | 0.03 | 0.26 | 0.37 | 0.08 | 0.21 | 0.38 |
wet | 0.12 | 0.53* | 0.52* | 0.58 ** | 0.46* | 0.44 | 0.61 ** | −0.21 | 0.55 * | ||
Forest | dry | −0.03 | −0.07 | 0.14 | 0.62 ** | 0.09 | −0.10 | 0.30 | −0.41 | 0.21 | |
wet | −0.53 * | 0.26 | 0.16 | 0.48* | 0.27 | −0.54 * | −0.33 | 0.09 | 0.02 | ||
Grass | dry | −0.09 | −0.08 | −0.22 | −0.64 ** | 0.03 | −0.04 | −0.12 | −0.06 | −0.44 | |
wet | 0.41 | −0.72 * | −0.42 | −0.66 ** | −0.44 | 0.27 | 0.07 | −0.08 | −0.49 * | ||
Water | dry | 0.61 ** | −0.38 | −0.29 | −0.85 ** | −0.06 | −0.24 | −0.23 | −0.26 | −0.50 * | |
wet | 0.89 ** | −0.27 | −0.55 * | −0.38 | −0.18 | 0.13 | −0.21 | −0.68 * | 0.18 | ||
Constr | dry | 0.13 | −0.48 * | 0.22 | 0.15 | −0.27 | 0.04 | −0.64 ** | −0.20 | −0.14 | |
wet | 0.24 | 0.18 | 0.36 | 0.27 | 0.12 | 0.21 | −0.24 | −0.20 | 0.49 * | ||
Substream | Argi | dry | −0.42 | −0.21 | −0.56 ** | 0.01 | −0.19 | −0.67 ** | −0.31 | −0.30 | −0.52 * |
wet | −0.04 | −0.61* | −0.56 ** | −0.43 | −0.49 * | −0.81 ** | −0.47 * | 0.26 | −0.61 ** | ||
Forest | dry | −0.15 | −0.33 | 0.15 | 0.49 * | −0.50 * | 0.30 | −0.31 | −0.13 | 0.36 | |
wet | −0.36 | 0.18 | 0.41 | 0.15 | −0.12 | 0.20 | −0.15 | −0.04 | 0.21 | ||
Grass | dry | 0.30 | 0.56 ** | 0.04 | −0.54 * | 0.47 * | 0.07 | 0.49 * | 0.47 * | 0.01 | |
wet | 0.24 | 0.06 | −0.19 | −0.07 | 0.24 | 0.19 | 0.53 * | 0.02 | −0.01 | ||
Water | dry | −0.13 | −0.13 | −0.37 | −0.58 ** | −0.04 | −0.15 | −0.12 | −0.03 | −0.52 * | |
wet | 0.40 | −0.82 * | −0.59 ** | −0.76 ** | −0.59 ** | −0.15 | −0.01 | −0.12 | −0.63 ** | ||
Constr | dry | −0.15 | −0.20 | 0.16 | 0.18 | 0.14 | −0.15 | −0.19 | −0.36 | 0.01 | |
wet | 0.01 | −0.02 | 0.16 | 0.41 | 0.08 | −0.26 | 0.21 | −0.13 | 0.04 |
Water Period | Spatial Scale | Water | Agri | Forest | Constr | Grass | Total |
---|---|---|---|---|---|---|---|
Dry | Riverine reach | 23.2 | 25.1 | 16.3 | 11.3 | 7.8 | 83.7 |
Riparian | 19.9 | 21.8 | 13.2 | 3.4 | 1.8 | 60.1 | |
Substream | 30.2 | 15.3 | 10.4 | 8.1 | 8.7 | 72.7 | |
Wet | Riverine reach | 30.7 | 22.8 | 11.8 | 7.3 | 4.6 | 77.2 |
Riparian | 26.6 | 12.0 | 5.9 | 0.8 | 12.5 | 57.8 | |
Substream | 22.8 | 21.6 | 15.7 | 3.5 | 1.6 | 65.2 |
Water Period | Spatial Scale | PD | LSI | LPI | CONTAG | SHDI | PRD | Total |
---|---|---|---|---|---|---|---|---|
Dry | Riverine reach | 33.1 | 13.8 | 5.0 | 5.3 | 5.7 | 11.1 | 74.0 |
Riparian | 7.6 | 2.2 | 15.1 | 30.6 | 1.8 | 12.4 | 69.7 | |
Substream | 24.6 | 8.8 | 23.9 | 6.4 | 4.2 | 2.9 | 70.8 | |
Wet | Riverine reach | 38.7 | 15.1 | 5.2 | 1.3 | 10.7 | 17.5 | 88.5 |
Riparian | 8.0 | 8.1 | 11.3 | 7.1 | 4.0 | 16.7 | 55.2 | |
Substream | 7.3 | 16.7 | 8.3 | 7.6 | 5.7 | 22.2 | 67.8 |
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Zhang, Y.; Zhao, Y.; Zhang, H.; Cao, J.; Chen, J.; Su, C.; Chen, Y. The Impact of Land—Use Composition and Landscape Pattern on Water Quality at Different Spatial Scales in the Dan River Basin, Qin Ling Mountains. Water 2023, 15, 3276. https://doi.org/10.3390/w15183276
Zhang Y, Zhao Y, Zhang H, Cao J, Chen J, Su C, Chen Y. The Impact of Land—Use Composition and Landscape Pattern on Water Quality at Different Spatial Scales in the Dan River Basin, Qin Ling Mountains. Water. 2023; 15(18):3276. https://doi.org/10.3390/w15183276
Chicago/Turabian StyleZhang, Yuanyuan, Yan Zhao, Huiwen Zhang, Jing Cao, Jingshu Chen, Cuicui Su, and Yiping Chen. 2023. "The Impact of Land—Use Composition and Landscape Pattern on Water Quality at Different Spatial Scales in the Dan River Basin, Qin Ling Mountains" Water 15, no. 18: 3276. https://doi.org/10.3390/w15183276